The performance predictor[1]} \(\mathrm {P}: \mathrm {C} \rightarrow \mathrm {R}\)  maps the latent representation of an architecture \(x\)  into its performance \(s_{x}\) . For an architecture \(x\)  and its performance \(s_{x}\)  as training data, the optimization of \(P\)  aims at minimizing the least-square regression \(\mbox{ loss }=\left(s_{x}-P(E(x))\right)^{2}\) .
